Summary of Rethinking Aleatoric and Epistemic Uncertainty, by Freddie Bickford Smith et al.
Rethinking Aleatoric and Epistemic Uncertainty
by Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses inconsistencies in the way researchers discuss probabilistic predictions in machine learning models, highlighting a lack of expressiveness in the traditional aleatoric-epistemic view. The authors propose a new framework for understanding different types of model-based uncertainties and their corresponding data-generating processes. By providing a clear delineation of these distinct quantities, the paper aims to improve communication within the field and facilitate progress. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us better understand how machine-learning models make predictions by clarifying confusing ideas about uncertainty. Uncertainty is important because it affects how well models perform. Researchers have talked about two types of uncertainty: aleatoric (random) and epistemic (related to knowledge). However, they haven’t been using these terms consistently. The paper fixes this problem by introducing a new way to think about model-based uncertainties. This will make it easier for researchers to talk about their ideas and work together. |
Keywords
» Artificial intelligence » Machine learning
